Surface acoustic wave technology as a tool for functional characterization of new compounds

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Soil phosphate detection and archaeology : in-stride phosphate detection and the elimination of arsenate interference to the malachite green method.

Archaeologists use soil analysis to detect chemicals, like phosphate, to indicate areas of anthropogenic activity. Phosphate detection is a multi-step process, which makes standard techniques time consuming. Kinetic studies decreased the analysis time for the malachite green (MG) method of phosphate detection. The 3-minute method allows extraction and analysis to be complete in 15 minutes. Continued studies resulted in two-color spectral monitoring, which provided values instantaneously. Arsenate (As(V)) interfere with the MG method and results in overestimation of phosphate. As(V) must be reduced to non-interfering arsenite. Two As(V) reducing agents--L-Cysteine and thiosulfate--were investigated. The thiosulfate method was suitable for field implementation with the 3-minute malachite green method. L-Cysteine is compatible with both MG time scales, but pre-reduction could not be improved beyond 20 minutes. The 3-minute malachite green method was utilized at an archaeological site in Virginia. The survey led to delineation of the site boundaries

Making Decisions with Unknown Sensory Reliability

To make fast and accurate behavioral choices, we need to integrate noisy sensory input, take prior knowledge into account, and adjust our decision criteria. It was shown previously that in two-alternative-forced-choice tasks, optimal decision making can be formalized in the framework of a sequential probability ratio test and is then equivalent to a diffusion model. However, this analogy hides a “chicken and egg” problem: to know how quickly we should integrate the sensory input and set the optimal decision threshold, the reliability of the sensory observations must be known in advance. Most of the time, we cannot know this reliability without first observing the decision outcome. We consider here a Bayesian decision model that simultaneously infers the probability of two different choices and at the same time estimates the reliability of the sensory information on which this choice is based. We show that this can be achieved within a single trial, based on the noisy responses of sensory spiking neurons. The resulting model is a non-linear diffusion to bound where the weight of the sensory inputs and the decision threshold are both dynamically changing over time. In difficult decision trials, early sensory inputs have a stronger impact on the decision, and the threshold collapses such that choices are made faster but with low accuracy. The reverse is true in easy trials: the sensory weight and the threshold increase over time, leading to slower decisions but at much higher accuracy. In contrast to standard diffusion models, adaptive sensory weights construct an accurate representation for the probability of each choice. This information can then be combined appropriately with other unreliable cues, such as priors. We show that this model can account for recent findings in a motion discrimination task, and can be implemented in a neural architecture using fast Hebbian learning

A Macro-Stochastic Approach to Improved Cost Estimation for Defense Acquisition Programs

Space is becoming increasingly congested as more objects are launched into orbit. The potential for a collision on orbit increases each time a new object enters space. This thesis presents a methodology to determine an optimal direction to maneuver a satellite that may be involved in a potential collision. The author presents a paradigm to determine the optimal direction of maneuver to achieve the lowest probability of collision, and examines how different magnitudes of a maneuver will affect the probability of collision. The methodology shows that if a satellite maneuvers in the optimal direction at any time during the orbit, except incremental periods and half periods, the probability of collision is reduced to a negligible amount. This provides a means to determine a maneuver direction and magnitude that will remove satellites from the potential collision area, while minimizing the resources necessary and maintaining mission quality

Informing Spacecraft Maneuver Decisions to Reduce Probability of Collision

Space is becoming increasingly congested as more objects are launched into orbit. The potential for a collision on orbit increases each time a new object enters space. This thesis presents a methodology to determine an optimal direction to maneuver a satellite that may be involved in a potential collision. The author presents a paradigm to determine the optimal direction of maneuver to achieve the lowest probability of collision, and examines how different magnitudes of a maneuver will affect the probability of collision. The methodology shows that if a satellite maneuvers in the optimal direction at any time during the orbit, except incremental periods and half periods, the probability of collision is reduced to a negligible amount. This provides a means to determine a maneuver direction and magnitude that will remove satellites from the potential collision area, while minimizing the resources necessary and maintaining mission quality

Wellbeing among U.S. Veterans: Results from the 2010 National Survey of Veterans

Our research focuses on self-rated general health and access to healthcare among veterans. We used data collected by the 2010 National Survey of Veterans, a nationally representative survey of veterans in the U.S. The purpose is to identify and assess aspects of military experiences which could be responsible for differences in veterans’ health and their access to healthcare. Specifically, we investigate how exposure to combat, as well as exposure to specific traumas, can have a lasting impact on the health of veterans. We utilized two nested regression models around our focal variables; a logistic regression model was used to assess the access to mental healthcare, while an ordinal regression model was used to assess self-rated general health. We were also able to infer that a structural change in policies for veterans’ healthcare might have provided significant benefits among the population. Findings show unique effects on health patterns for combat and trauma in the field. Paradoxically, we also observe that many of the socio-economic indicators operated quite differently than they do for the general population in the United States in terms of their links to health differences

A normative approach to radicalization in social networks

In recent decades, the massification of online social connections has made information globally accessible in a matter of seconds. Unfortunately, this has been accompanied by a dramatic surge in extreme opinions, without a clear solution in sight. Using a model performing probabilistic inference in large-scale loopy graphs through exchange of messages between nodes, we show how circularity in the social graph directly leads to radicalization and the polarization of opinions. We demonstrate that these detrimental effects could be avoided by actively decorrelating the messages in social media feeds. This approach is based on an extension of Belief Propagation (BP) named Circular Belief Propagation (CBP) that can be trained to drastically improve inference within a cyclic graph. CBP was benchmarked using data from Facebook and Twitter. This approach could inspire new methods for preventing the viral spreading and amplification of misinformation online, improving the capacity of social networks to share knowledge globally without resorting to censorship.Comment: 23 pages, 8 figures, 1 supplementary materia